Research background: Digital transformation across the European Union (EU) has become a critical driver of economic competitiveness and societal development. However, there are still notable differences in member states’ infrastructure, governance, and level of digital preparedness. Interest in setting the primary factors that determine digital maturity and understanding the structural disparities in the EU’s digital landscape is developing as a result of Industry 5.0, which places a high priority on human-centricity, sustainability, and resilience. Purpose of the article: The objective of this article is to evaluate the digital preparedness of EU member states by using the most recent Network Readiness Index (NRI) data for 2024. It aims to determine the factors that have the most significant impact on the overall digital performance, evaluate the disparities between countries in the quartiles of the NRI rankings, and visualize the current status of digital transformation throughout the EU. This research further contextualizes the findings within the shift from Industry 4.0 to Industry 5.0. Methods: The study applies a combination of multivariate and inferential statistical techniques. A segmentation map is used to visually group countries by digital performance. Stacked bar graphs illustrate the importance of specific NRI components to overall scores. An independent sample t-test is performed to assess significant differences between high-performing and moderately performing countries. A regression study determines the primary predictors of overall digital readiness. Findings & value added: The study indicates significant variability in the structure and determinants of digital readiness among EU nations. Research and development (R&D) investment, household Internet access, and GDP per capita in purchasing power standards (PPS) are considered the most reliable indicators of digital success. Statistically significant differences were established between the first and second quartiles across various parameters of the NRI. This study improves the literature by delivering a thorough empirical evaluation of the 2024 NRI data and presenting practical recommendations for policymakers aiming to promote fair and sustainable digital transformation in accordance with Industry 5.0 principles.

Sustainability is about intergenerational solidarity in finding solutions to ensure the continued growth that allows organizations and companies to be proactive. In the context of contemporary technology, the fourth industrial revolution, i.e., the widely accepted notion of Industry 4.0, and swift corporate growth; the effects of industry, transportation, and urbanization on climate change; and the preservation of the environment in its entirety are becoming more and more significant. The primary objective of this study is to evaluate the eco-innovation performance of European nations, comparing their evolution from 2013 to 2022, in order to identify patterns, leaders, and opportunities for improvement in the implementation of eco-innovation strategies. This study includes the analysis of the Eco-Innovation Index as well as other studies carried out at the European Union level in the field of sustainability and sustainable innovation through the graphic maps, cluster analysis, and correspondence analysis in the statistical program SPSS. Different approaches to development are taken by the EU’s member states. Even though some countries have achieved significantly lower results than the average of the Eco-Innovation Index or significantly higher results than the average, overall, all countries (except Romania) have improved their point evaluations when compared to the year 2013 when the Eco-Innovation Index started to be reported. The nations in the Northern European region produce consistent, long-term outcomes, and these countries have emerged as leaders in innovation in recent years. The countries of Luxembourg, Austria, Finland, Sweden, and Denmark have the best long-term outcomes. On the contrary, Bulgaria has ranked last for a long time, with the least-rated eco-innovation activities.

This study examines small and medium-sized enterprises (SMEs) in the European Union (EU) and their openness to circular economy practices. A cross-section of European SMEs is evaluated using data for the EU-27 taken from the Euroflash Barometer 549 (SMEs, resource efficiency, and green markets). The aim of this study is to reveal the relationship between various circular economy activities within the European Union. This objective is accomplished by means of the statistical technique known as principal component analysis (PCA), which involves the categorization of variables into dimensions. The analysis pointed to two categories of circular economy activities. According to the research results, up to 93% of SMEs implement at least one circular economy activity. SMEs that adopt strategies for the efficient use of environmental resources, particularly in water conservation, also employ energy-saving measures. PCA identified a second group prioritizing modular architectures, environmentally benign materials, and recurrent resource use. Notably, businesses focusing on water conservation appear to prioritize sustainable product design the least. In contrast, those incorporating circular economy principles into product design and manufacturing also tend to emphasize recycling and reuse.

Innovation is a critical element in numerous domains, especially when it comes to determining the course of business success via efficient innovation management systems. The objective of this study is to determine which innovation category has the greatest impact on the effectiveness of innovation management by analyzing the correlation of innovation and its associated activities on the innovation performance of companies. This study synthesizes empirical research findings regarding the relationship between management and innovation performance through the utilization of meta-analysis. In recent decades, meta-analysis has gained significant prominence as a method to improve the precision of results by integrating multiple studies into a comprehensive analysis. Systematic reviews and meta-analyses enhance the credibility of research outcomes due to their status as the most dependable forms of evidence. A noteworthy correlation is observed between innovation performance and innovation categories, with small and medium-sized enterprises (SMEs) exhibiting the strongest correlation. Significantly, organizational innovations demonstrate the strongest correlation coefficient, indicating that they have the greatest impact on innovation performance. This research highlights the significance of innovation management in effectively addressing enterprise challenges and promoting business success. It specifically emphasizes the critical role that organizational innovations play in facilitating innovation performance.

Artificial intelligence (AI) is becoming a significant driver of transformation in the business environment, particularly for startups, which are characterised by high levels of adaptability, innovation orientation and strategic flexibility. Startups that incorporate artificial intelligence into their structures and processes are reshaping traditional approaches to value creation. This study aims to explore how artificial intelligence influences the formation and evolution of business models in startups across different industries. The analysis draws on academic literature, case study evidence and current market observations in order to identify key areas where artificial intelligence may affect fundamental business model components. The comparative part of the study focuses on differences between AI-driven startups and traditional companies that did not emerge as startups and do not rely on artificial intelligence as a core strategic technology. The comparison is carried out within three selected industries, namely banking, automotive manufacturing and retail. The study applies the Business Model Canvas (BCM) as a conceptual tool to evaluate the configuration of business models in both categories of companies. The results of the analysis indicate that startups using artificial intelligence are creating new types of business models in which AI plays a significant role in shaping the value proposition, sales channels, and revenue streams. Traditional companies, on the other hand, are not transforming their business models radically but are integrating artificial intelligence mainly in support areas. Building on these findings, this research contributes to the broader understanding of business model innovation under the influence of artificial intelligence and outlines the analytical foundations for identifying structural distinctions between AI-driven startups and traditional companies.

The sharing economy, as a developing business model, is becoming more and more popular. This phenomenon can be observed in many countries. The sharing economy, as a part of human life in its essence, has existed for a long time. In general, the sharing economy is considered an economic model where individuals or organizations share a certain type of property or, in this way, provide services for a financial reward. One of the most used sectors, where there is constant interest, is the shared transport of people, goods, and services. However, platforms operating in the shared transportation segment should be analysed from various points of view, such as their financial results and overall economic stability. The study is primarily focused on the sector of shared transport and transport services. The chief objective of the study was to demonstrate the impact of the COVID-19 pandemic on shared transport and transport services within the territory of the Slovak Republic. The analysis included ride-hailing platforms such as Bolt Services SK as well as food and dining delivery platforms such as Wolt Slovakia. Through appropriately chosen methods of financial and economic analysis, the situation of the platforms before and during the COVID-19 pandemic was determined. We also performed an analysis of the development of the number of cars and the related level of automobilization in the territory of the Slovak Republic, since the shared economy represents an alternative to conventional economic models.